Statistics

University of Calcutta (informally known as Calcutta University or CU) is a collegiate public state university located in Kolkata, West Bengal, India.&nbsp;Within India it is recognized as a &quot;Five-Star University&quot; and accredited &quot;A&quot; Grade by National Assessment and Accreditation Council.

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The University of Calcutta has secured the Fifth position among the Universities of India in the prestigious &quot;Indian University Ranking 2019&quot; list, released by the NIRF of the Ministry of Human Resource Development, Government of India.

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It was established on 24 January 1857, and was one of the first institutions in Asia to be established as a multidisciplinary and Western-style university. Its alumni and faculty include several heads of state, heads of government, social reformers, prominent artists, only academy award winner and Dirac medal winner in India, many Fellows of the Royal Society and five Nobel laureates as of 2019 which is highest in South Asia.

University Of Calcutta

This article describes how Universal Mobile Telecommunications System (UMTS) is a 3G cellular network standard that uses location update (LU) and location search (LS) for mobility management. LU requires update in two location registers (LRs) - home LR (HLR) and visiting LR (VLR) - where one HLR controls multiple VLRs. HLR updates (HLRUs) are costlier, and hence should be less frequent. To achieve it, VLRs maintain a forward pointer chain among themselves, instead of updating HLR. But a lengthy chain increases LS cost considerably. So, we propose to restrict the chain length to unity by introducing an additional backward pointer, and using the concept of root-VLR similar to the home agent concept of mobile IP. We derive a closed form solution to approximate the average HLRUs per call, and substantiate it by simulation in cases of both random and diurnal mobilities. Results reveal substantial reduction in HLRUs per call, even when call-to-mobility ratio is high. UMTS operators will find the analysis worth considering, while managing their 3G networks. Copyright © 2018, IGI Global.

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International Journal of Business Data Communications and Networking

Analytical study of a dual pointer based strategy for location update using node B sojourn time in UMTS networks

A class of covariate-adjusted response-adaptive randomization procedures is developed for binary treatment outcomes in a phase III clinical trial set up involving multiple treatments. The target allocation is developed by combining the ethical aspects with statistical precision under the existence of treatment covariate interaction. Relevant measures of the performance for the proposed allocation designs are studied and compared. © 2018, © 2018 Taylor & Francis.

Journal of Biopharmaceutical Statistics

A class of Covariate-Adjusted Response-Adaptive Allocation Designs for Multitreatment Binary Response Trials

An optimal target proportion is derived balancing between the needs of clinician and statistician considering a general criterion and binary treatment outcome. A response-adaptive procedure based on this optimal proportion is developed and the associated limiting results are derived. In addition, the proposed procedure is compared with the existing competitors. © 2018, © 2018 American Statistical Association.

GENERAL

Statistics in Biopharmaceutical Research

An Optimal Three Treatment Allocation for Binary Treatment Responses

A two treatment response adaptive design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis. © 2018, © 2018 Taylor & Francis.

A response adaptive design for ordinal categorical responses

We intend to discuss some standard and non-standard Data Integration Techniques with a hypothetical illustration from a study of environmental factors [to be called sources] and the extent of their toxic release in the surface areas of several locations in a closed region. The purpose is to rank the locations&mdash;by &rsquo;integrating&rsquo; the effects of toxic release from all the potential sources. From a practical point of view, it is highly unlikely that a single location can be the &rsquo;absolute best&rsquo; [i.e., the location possesses minimum toxic release for each of the toxicity sources] for any reasonable environmental study. Hence, there is a need for data integration to arrive at an overall ranking of the locations, keeping the differential toxic effects in mind. Naturally, rank 1 corresponds to the least &rsquo;compound&rsquo; effect of the toxicity sources.

Statistical Methods in Social Science Research

Data Integration Techniques

Social mobility is the movement of individuals, families, households, or other categories of people within or between social strata in a society. Societies organized by social class, rather than caste, usually allow greater social mobility; in such societies, one&rsquo;s ability to achieve a higher social status can depend on factors such as social connections, wealth, effort, and education. In order to study the dynamics of social processes, it is natural to start by looking at the movement of people between social classes. Since such moves are largely unpredictable at the individual level, it is necessary for a model to describe mechanism of movement in probabilistic terms. A distinction has to be made between intergenerational mobility and intra-generational mobility. The former refers to social mobility which reflects changes of social class from one generation to another. Intra-generational mobility refers to changes of classes which take place during an individual&rsquo;s life span. This type of movement is called occupational or labor mobility since it is usually more directly concerned with occupations. Many deterministic and stochastic models have been developed to study social and occupational mobility situations in the different parts of the world.

Social and Occupational Mobility

There is an impressive published literature on the statistical issues of assessment of agreement among two or more raters involving both qualitative and quantitative data. We propose to present basic ideas and relevant statistical methods toward assessment of agreement.

This chapter draws material from co-published work of one of the authors: &lsquo;Some further aspects of assessment of agreement involving bivariate normal responses,&rsquo; published in Int&rsquo;l Jour. of Statistical Sciences, Vol. 13, 2013, pp. 1&ndash;19. Portions have been used here with permission from the Author Dr. Ganesh Dutta as well as Publisher.

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Statistical Assessment of Agreement

From some basic considerations of formation of dyadic [pair-wise] binary [go/no-go] relations among a given number of units [individuals/households etc], we develop a social network. Basic parameters are then introduced and their quantifications are made for a given population network. Then a sampling problem is considered for formation of a sample network. Estimation of the population parameters based on data arising from a sample network are then discussed in details.

Social Network Analysis

Principal component analysis (PCA) is a method for dimension reduction tool in order to reduce a large set of variables to a small set of components that still contains most of the information in the original data set. Under PCS, we transform a number of correlated variables into a smaller set of uncorrelated components called principal components.

Principal Component Analysis

Clustering can be considered to be the most important unsupervised learning technique to find homogeneous groups in a collection of a moderately large number of data points. Clustering could be defined as the process of dividing items into unknown number of groups whose members are alike in some way. A cluster is therefore a collection of items those are similar among themselves and are dissimilar to the items belonging to other clusters. It can be shown that there is no absolute &quot;best&quot; criterion which would be independent of the final aim of the clustering. Hence, the structure of the clusters should be finalized by the user depending on the physical requirements. By depending on the nature of analysis, clustering is called an unsupervised learning method and classification is called a supervised learning method.

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